---
title: "RED WINE DATASET ANALYSIS"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    social : menu
    theme : simplex
    storyboard : TRUE
    source_code : embed
---

```{r setup, include=FALSE}
libraries= c("dplyr","lattice","ggplot2","DT","flexdashboard","FactoMineR","GGally")
lapply(libraries,require,character.only=TRUE)
df=read.csv("winequality_red.csv")
attach(df)
```



# INTRO {.tabset}
==============================================

The analysis is aimed at looking and finding deep insights of the red wine dataset. The report also aims in bringing a conclusion so as to what are the factors that affect the quality of wine. 
The features of the dataset are as follows , This dataset has 12 variables which contains 7 variables of ingredient, 3 variables of physical properity, 1 variable of alcohol and 1 of quality.
The dataset are of the size `1599 , 12`. The libraries used for the data are , tidyverse and ggplot. The libraries are used for visualization which can help us to come to conclusion. 

The data is in csv format.In computing, a comma-separated values (CSV) file stores tabular data (numbers and text) in plain text. Each line of the file is a data record. Each record consists of one or more fields, separated by commas. The columns are as follows:


[1] "fixed.acidity"       
[2]"volatile.acidity"     
[3]"citric.acid"         
[4]"residual.sugar"      
[5] "chlorides"           
[6]"free.sulfur.dioxide" 
[7]"total.sulfur.dioxide"
[8]"density"             
[9] "pH"                   
[10]"sulphates"
[11]"alcohol" 
[12]"quality"  

--------------------------------------------------
----------------------------------------------
     
# MENU 
### Structure
```{r}
str(df)
```

### SUMMARY

```{r}
summary(df)
```



# UNIVARIATEPLOTS 
### PLOTS FOR EACH FACTORS

```{r}
ggplot(df,aes(quality))+geom_bar()+ggtitle("Quality counts graph")
```

Column {data-width=650}
--------------------------




```{r}
ggplot(df,aes(fixed.acidity))+geom_histogram(color="black",fill="white",binwidth = 0.1)+ggtitle("Fixed Acidity Value count")
```

Column {data-width=650}
-----------------------------------------------------------------------

```{r}
ggplot(df,aes(volatile.acidity))+geom_histogram(color="black",fill="blue",binwidth = 0.1)+ggtitle("Volatile Acidity count graph")
```

Column {data-width=650}
-----------------------------------------------------------------------

```{r}
ggplot(df,aes(citric.acid))+geom_histogram(color="red",fill="white",bins = 30)+ggtitle("Citric Acid Count graph")
```

Column {data-width=650}
-----------------------------------------------------------------------

```{r}
ggplot(df,aes(residual.sugar))+geom_histogram(color="red",fill="blue",binwidth = 0.5)+ggtitle("Residual Sugar Count")
```

Column {data-width=650}
-----------------------------------------------------------------------

```{r}
ggplot(df,aes(chlorides))+geom_histogram(color="black",fill="yellow",binwidth = 0.01)+ggtitle("Chloride Count")
```

Column {data-width=650}
-----------------------------------------------------------------------

```{r}
summary(df$chlorides)
```



```{r}
ggplot(df , aes(alcohol)) + geom_histogram(color="brown",fill="orange",binwidth = 0.1)+ggtitle("Alcohol count")

```

Column {data-width=650}
-----------------------------------------------------------------------

```{r}
summary(df$alcohol)
```



```{r}
ggplot(df,aes(free.sulfur.dioxide))+geom_histogram(color="black",fill="blue",binwidth = 1)+ggtitle("Free Sulpher Dioxide count")
```

Column {data-width=650}
-----------------------------------------------------------------------

```{r}
ggplot(df,aes(total.sulfur.dioxide))+geom_histogram(color="black",fill="white",binwidth = 2)+ggtitle("Total Sulfur dioxide count")
```

Column {data-width=650}
-----------------------------------------------------------------------

```{r}
ggplot(df,aes(pH))+ggtitle("pH count ")+geom_histogram(color="black",fill="white",binwidth = 0.05)
```

Column {data-width=650}
-----------------------------------------------------------------------

```{r}
ggplot(df,aes(density))+ggtitle("Density Count")+geom_histogram(color="black",fill="white",binwidth = 0.001)
```

Column {data-width=650}
-----------------------------------------------------------------------

```{r}
ggplot(df,aes(sulphates))+geom_histogram(color="black",fill="white",binwidth = 0.01 )+ggtitle("Sulphates count")
```





# OUTLIERS  
### REMOVING THE OUTLIERS FROM PARAMETERS FOR BETTER ANALYSIS
```{r}
df_rmout = subset(df,residual.sugar < 15)
df_rmout = subset(df,total.sulfur.dioxide < 289) 
df_rmout = subset(df,density < 1.0037)
df_rmout = subset(df,free.sulfur.dioxide < 72)
```


```{r}
summary(df_rmout)
```






# NEW VARIATE 



```{r}
ggplot(df_rmout,aes(quality))+geom_bar()+ggtitle("Quality counts graph")
```

Column {data-width=650}
-----------------------------------------------------------------------

```{r}
ggplot(df_rmout,aes(fixed.acidity))+geom_histogram(color="yellow",fill="red",binwidth = 0.1)+ggtitle("Fixed Acidity Value count")
```

Column {data-width=650}
-----------------------------------------------------------------------



```{r}
ggplot(df_rmout,aes(volatile.acidity))+geom_histogram(color="black",fill="white",binwidth = 0.1)+ggtitle("Volatile Acidity count graph")
```

Column {data-width=650}
-----------------------------------------------------------------------


```{r}
ggplot(df_rmout,aes(citric.acid))+geom_histogram(color="black",fill="white",bins = 30)+ggtitle("Citric Acid Count graph")
```

Column {data-width=650}
-----------------------------------------------------------------------


```{r}
ggplot(df_rmout,aes(residual.sugar))+geom_histogram(color="black",fill="white",binwidth = 0.5)+ggtitle("Residual Sugar Count")
```

Column {data-width=650}
-----------------------------------------------------------------------


```{r}
ggplot(df_rmout , aes(alcohol)) + geom_histogram(color="black",fill="white",binwidth = 0.1)+ggtitle("Alcohol count")
```



# WINEQUALITY 


### The ratio calculations are free sulpher dioxide / total sulpher dioxide

```{r}

df_rmout$class = cut(df_rmout$quality,breaks=c(0,4,6,9),labels=c("average","good","great"))
df_rmout$ratio = (df_rmout$free.sulfur.dioxide/df_rmout$total.sulfur.dioxide)
```


```{r}
ggplot(df_rmout,aes(class))+geom_bar(col=c("yellow","red","green"),fill=c("blue","green","red"))

```





# BIVARIATE  
### WINE QUALITY VS CLASS

```{r}
ggcorr(df_rmout)
```

Column {data-width=650}
-----------------------------------------------------------------------

```{r}
ggplot(df_rmout,aes(class,alcohol))+geom_boxplot()
```

Column {data-width=650}
-----------------------------------------------------------------------

```{r}
ggplot(df_rmout,aes(alcohol,fill=class))+geom_histogram(aes(x = alcohol , y = ..density..),binwidth = 0.1)+geom_density(alpha=0.2)
```

Column {data-width=650}
-----------------------------------------------------------------------

```{r}
ggplot(df_rmout,aes(alcohol,fill=class))+geom_density(alpha=0.2)
```

Column {data-width=650}
-----------------------------------------------------------------------

```{r}
print(quantile(df_rmout[df_rmout$class=="average",]$alcohol))
print(quantile(df_rmout[df_rmout$class=="good",]$alcohol))
print(quantile(df_rmout[df_rmout$class=="great",]$alcohol))
```


### Total Sulpher Dioxide vs class of wine

```{r}
ggplot(df_rmout,aes(class,free.sulfur.dioxide))+geom_boxplot()
```

Column {data-width=650}
-----------------------------------------------------------------------

```{r}
ggplot(df_rmout,aes(residual.sugar,fill = class))+geom_density(alpha =0.5)
```

Column {data-width=650}
-----------------------------------------------------------------------

### DENSITY VS RESIDUAL SUGAR

```{r}
ggplot(df_rmout,aes(residual.sugar,fill = class))+geom_density(alpha = 0.5)
```

Column {data-width=650}
-----------------------------------------------------------------------

```{r}
ggplot(df_rmout,aes(class , residual.sugar))+geom_boxplot()
```

Column {data-width=650}
-----------------------------------------------------------------------

```{r}
cor(df_rmout$density,df_rmout$residual.sugar)
```

Column {data-width=650}
-----------------------------------------------------------------------

### DENSITY VS QUALITY

```{r}
ggplot(df_rmout,aes(density,fill = class))+geom_density(alpha = 0.5)
```

Column {data-width=650}
-----------------------------------------------------------------------

```{r}
ggplot(df_rmout,aes(class ,density))+geom_boxplot()
```



# SCATTER PLOTS 

```{r}
ggplot(df_rmout,aes(alcohol,density))+geom_point()
```

Column {data-width=650}
-----------------------------------------------------------------------

```{r}
ggplot(df_rmout,aes(residual.sugar,density))+geom_point()
```


# DATATABLE 

### Valuebox
```{r}
value=sum(density)
valueBox("count of density",value,icon="fa fa-user")
```



### DOWNLOAD
```{r}
datatable(df,extensions='Buttons',options=list(dom="Bftrip",Buttons=c('copy','print','csv','pdf')))
```

# INSIGHTS 
1.There is a negative relation between density and alcohol which seems to be the case so as to why great wines have lower density and higher alcohol concentration as well as lower SO2 levels(free and total). 

2.Our analysis proves that , Better wines are bound to have a control on alcohol level and SO2 level and since they use more concentrated alcohol they're bound to have a lower density. Since they tend to have a lower density they also tend to have lower residual sugar. 


3.It is noted that alcohol has a huge impact on the overall quality of wine.

4.Apart from alcohol sulphur dioxide free and total had infuence on the wine being great. The chemical aspects of wine affect the quality of wine. 

5.It is also also found that there was a specific pH range and ratio of the sulphur dioxide affecting the entire quality of wine along with the alcohol concentration.